date: 2020-03-17T11:08:07Z pdf:PDFVersion: 1.4 pdf:docinfo:title: Gaussian Process Panel Modeling?Machine Learning Inspired Analysis of Longitudinal Panel Data xmp:CreatorTool: LaTeX with hyperref package + hypdvips access_permission:can_print_degraded: true subject: In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. language: en dc:format: application/pdf; version=1.4 pdf:docinfo:creator_tool: LaTeX with hyperref package + hypdvips access_permission:fill_in_form: true pdf:encrypted: false dc:title: Gaussian Process Panel Modeling?Machine Learning Inspired Analysis of Longitudinal Panel Data modified: 2020-03-17T11:08:07Z cp:subject: In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. pdf:docinfo:subject: In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. pdf:docinfo:creator: Andreas M. Brandmaier meta:author: Andreas M. Brandmaier meta:creation-date: 2020-03-17T06:00:49Z created: 2020-03-17T06:00:49Z access_permission:extract_for_accessibility: true Creation-Date: 2020-03-17T06:00:49Z Author: Andreas M. Brandmaier producer: dvips + MiKTeX GPL Ghostscript 9.0 pdf:docinfo:producer: dvips + MiKTeX GPL Ghostscript 9.0 pdf:unmappedUnicodeCharsPerPage: 0 Keywords: longitudinal analysis, machine learning, statistical learning, Bayesian, continuous-time, prediction access_permission:modify_annotations: true dc:creator: Andreas M. Brandmaier dcterms:created: 2020-03-17T06:00:49Z Last-Modified: 2020-03-17T11:08:07Z dcterms:modified: 2020-03-17T11:08:07Z title: Gaussian Process Panel Modeling?Machine Learning Inspired Analysis of Longitudinal Panel Data xmpMM:DocumentID: 267adc98-6a70-11ea-0000-fd0b11513fae Last-Save-Date: 2020-03-17T11:08:07Z pdf:docinfo:keywords: longitudinal analysis, machine learning, statistical learning, Bayesian, continuous-time, prediction pdf:docinfo:modified: 2020-03-17T11:08:07Z meta:save-date: 2020-03-17T11:08:07Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Andreas M. Brandmaier dc:language: en dc:subject: longitudinal analysis, machine learning, statistical learning, Bayesian, continuous-time, prediction access_permission:assemble_document: true xmpTPg:NPages: 20 pdf:charsPerPage: 3491 access_permission:extract_content: true access_permission:can_print: true meta:keyword: longitudinal analysis, machine learning, statistical learning, Bayesian, continuous-time, prediction access_permission:can_modify: true pdf:docinfo:created: 2020-03-17T06:00:49Z